Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images

Author:

Wang Weiwei,Zhao Yuanshen,Teng Lianghong,Yan Jing,Guo Yang,Qiu Yuning,Ji Yuchen,Yu Bin,Pei Dongling,Duan Wenchao,Wang Minkai,Wang Li,Duan JingxianORCID,Sun Qiuchang,Wang Shengnan,Duan Huanli,Sun Chen,Guo Yu,Luo Lin,Guo Zhixuan,Guan Fangzhan,Wang Zilong,Xing Aoqi,Liu Zhongyi,Zhang Hongyan,Cui Li,Zhang Lan,Jiang Guozhong,Yan Dongming,Liu Xianzhi,Zheng HairongORCID,Liang Dong,Li Wencai,Li Zhi-ChengORCID,Zhang ZhenyuORCID

Abstract

AbstractCurrent diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort (n = 1362) and a validation cohort (n = 340), and tested on an internal testing cohort (n = 289) and two external cohorts (n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary

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